diff --git a/.github/workflows/update_hpc_requirements.yml b/.github/workflows/update_hpc_requirements.yml index 7e09b14..38dbc42 100644 --- a/.github/workflows/update_hpc_requirements.yml +++ b/.github/workflows/update_hpc_requirements.yml @@ -28,7 +28,7 @@ jobs: uses: astral-sh/setup-uv@v5 - name: Regenerate env/hpc/requirements.txt - run: uv run scripts/export_hpc_requirements.py + run: uv run scripts/hpc/export_requirements.py - name: Commit updated requirements if changed uses: stefanzweifel/git-auto-commit-action@v5 diff --git a/configs/hpc/smoke_test.yaml b/configs/hpc/smoke_test.yaml index 720f829..1dd2fc0 100644 --- a/configs/hpc/smoke_test.yaml +++ b/configs/hpc/smoke_test.yaml @@ -1,5 +1,5 @@ # Minimal config to verify HPC setup is functional. -# Run with: python src/train.py --config-path configs/hpc/smoke_test.yaml +# Run with: python scripts/train.py --config-path configs/hpc/smoke_test.yaml exp_name: "hpc_smoke_test" seed: 0 track: false # Test WandB integration diff --git a/docs/HPC.md b/docs/HPC.md index ff18a5f..3e0e8d1 100644 --- a/docs/HPC.md +++ b/docs/HPC.md @@ -79,7 +79,7 @@ After installation, run these commands to ensure your environment is set up corr `env/hpc/requirements.txt` is auto-generated from `pyproject.toml`. To regenerate: ```bash -uv run scripts/export_hpc_requirements.py +uv run scripts/hpc/export_requirements.py ``` Modules listed in `env/hpc/modules.txt` are automatically excluded from the pip requirements to save space and use HPC-optimized binaries. diff --git a/docs/api/simulate.md b/docs/api/simulate.md new file mode 100644 index 0000000..9c4b21b --- /dev/null +++ b/docs/api/simulate.md @@ -0,0 +1,14 @@ +# Training and Simulation for Brittle Star Models + +## Simulating a model + +In order to simulate and view the behavior of a trained model, you can use the `simulate.py` script. This script allows you to specify the path to a trained model and will launch a simulation using that model. This script has the following parameters: + +- `--model`: The path to the trained model artifact to simulate. +- `--model-type`: The type of model to simulate (e.g., `random`, ...) +- `--task`: The task to simulate (e.g., `directed_locomotion`, ...) +- `--seed`: The random seed for reproducibility. + +```bash +python simulate.py --model artifacts/my_model --model-type random --task directed_locomotion --seed 0 +``` \ No newline at end of file diff --git a/docs/api/train_simulate.md b/docs/api/train_simulate.md deleted file mode 100644 index 9b299a9..0000000 --- a/docs/api/train_simulate.md +++ /dev/null @@ -1,30 +0,0 @@ -# Training and Simulation for Brittle Star Models - -## Training a model - -To train a model, you can use the `train.py` script. This script allows to pass some parameters to customize the training process: - -- `--out`: The output path where the trained model will be saved. -- `--model_type`: The type of model to train (e.g., `random`, ...) -- `--task`: The task to train on (e.g., `directed_locomotion`, ...) -- `--seed`: The random seed for reproducibility. -- `--epochs`: The number of epochs to train for. - -This will then train the specified model on the specified task for the given number of epochs and save the trained model to the specified output path. - -```bash -python train.py --out artifacts/my_model --model-type random --task directed_locomotion --seed 0 --epochs 50 -``` - -## Simulating a model - -In order to simulate and view the behavior of a trained model, you can use the `simulate.py` script. This script allows you to specify the path to a trained model and will launch a simulation using that model. This script has the following parameters: - -- `--model`: The path to the trained model artifact to simulate. -- `--model-type`: The type of model to simulate (e.g., `random`, ...) -- `--task`: The task to simulate (e.g., `directed_locomotion`, ...) -- `--seed`: The random seed for reproducibility. - -```bash -python simulate.py --model artifacts/my_model --model-type random --task directed_locomotion --seed 0 -``` \ No newline at end of file diff --git a/ruff.toml b/ruff.toml index d665ebb..db1bcd3 100644 --- a/ruff.toml +++ b/ruff.toml @@ -394,3 +394,6 @@ extend-ignore = [ "PLW0603", # global-statement # "PLW1404", # implicit-str-concat ] + +[lint.per-file-ignores] +"__init__.py" = ["F401"] diff --git a/scripts/export_hpc_requirements.py b/scripts/hpc/export_requirements.py similarity index 98% rename from scripts/export_hpc_requirements.py rename to scripts/hpc/export_requirements.py index 9ff3de7..5908ad3 100644 --- a/scripts/export_hpc_requirements.py +++ b/scripts/hpc/export_requirements.py @@ -5,9 +5,6 @@ This is a LOCAL DEVELOPER UTILITY — run it on your own machine before pushing code whenever pyproject.toml dependencies change. It reads the modules from env/hpc/modules.txt and the full dependency list from pyproject.toml, then writes the remainder to env/hpc/requirements.txt. - -Usage: - uv run scripts/export_hpc_requirements.py """ from __future__ import annotations diff --git a/experiments/simulate.py b/scripts/simulate.py similarity index 100% rename from experiments/simulate.py rename to scripts/simulate.py diff --git a/scripts/train.py b/scripts/train.py new file mode 100644 index 0000000..9ee6da6 --- /dev/null +++ b/scripts/train.py @@ -0,0 +1,75 @@ +import subprocess +import time + +import torch +import tyro +import yaml +import os + +from brittle_star_project.dataclasses import PPOArgs +from brittle_star_project.trainers.PPOTrainer import PPOTrainer +from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper + + +def make_env(config_path: str | None, num_envs: int) -> BrittleStarJaxEnvWrapper: + if config_path is None: + return BrittleStarJaxEnvWrapper.default(num_envs=num_envs) + return BrittleStarJaxEnvWrapper.from_config(config_path, num_envs=num_envs) + + +def parse_args(log: bool = True) -> PPOArgs: + temp_args = tyro.cli(PPOArgs) + + if temp_args.hyperparameter_config_path is not None: + if log: + print(f"Loading hyperparameter config from {temp_args.hyperparameter_config_path}") + + with open(temp_args.hyperparameter_config_path, "r") as f: + config = yaml.safe_load(f) + if config: + # parse PPOArgs with defaults from yaml. + for key, value in config.items(): + if hasattr(temp_args, key): + setattr(temp_args, key, value) + + # Reparse CLI to ensure they OVERRIDE the yaml + args = tyro.cli(PPOArgs, default=temp_args) + else: + if log: + print("No hyperparameter config provided, using default config") + + args = temp_args + return args + + +def get_git_hash() -> str: + try: + return ( + subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip() + ) + except subprocess.CalledProcessError | UnicodeDecodeError: + return "none" + + +if __name__ == "__main__": + args = parse_args() + + args.batch_size = args.num_envs * args.num_steps + args.minibatch_size = args.batch_size // args.num_minibatches + args.num_iterations = args.total_timesteps // args.batch_size + + git_hash = get_git_hash() + run_name = f"{args.exp_name}__seed_{args.seed}__{git_hash}__{int(time.time())}" + if args.run_dir is None: + run_dir = f"runs/{run_name}" + else: + run_dir = args.run_dir + + os.makedirs(run_dir, exist_ok=True) + + env = make_env(args.env_config_path, args.num_envs) + + torch.backends.cudnn.deterministic = args.torch_deterministic + + ppo_trainer = PPOTrainer(args, env, run_dir, run_name) + ppo_trainer.train() diff --git a/src/brittle_star_project/dataclasses/PPOArgs.py b/src/brittle_star_project/dataclasses/PPOArgs.py index 6d8b9b4..3f04c16 100644 --- a/src/brittle_star_project/dataclasses/PPOArgs.py +++ b/src/brittle_star_project/dataclasses/PPOArgs.py @@ -1,6 +1,9 @@ from dataclasses import dataclass +import jax + +@jax.tree_util.register_dataclass @dataclass class PPOArgs: """ @@ -10,6 +13,9 @@ class PPOArgs: # path to environment config file, if None, use default config env_config_path: str | None = None + # path to hyperparameter config file (yaml), if None, use default config + hyperparameter_config_path: str | None = None + # the name of this experiment exp_name: str = "brittle_star_ppo" diff --git a/src/brittle_star_project/trainers/PPOTrainer.py b/src/brittle_star_project/trainers/PPOTrainer.py new file mode 100644 index 0000000..7df57f0 --- /dev/null +++ b/src/brittle_star_project/trainers/PPOTrainer.py @@ -0,0 +1,536 @@ +import datetime +import random +import sys +import time +from dataclasses import asdict, dataclass +from functools import partial +from typing import Any + +import flax +import jax +import jax.numpy as jnp +import numpy as np +import optax +import tqdm +from flax.training.train_state import TrainState +from torch.utils.tensorboard import SummaryWriter + +from brittle_star_project.dataclasses import EpisodeStatistics, PPOArgs +from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper +from MLPs.mlps import ( + Actor, + AgentParams, + GenericDenseLayersWithActivation, + OneDenseLayerMLP, + Storage, +) +from ppo import PPO + + +@jax.jit +def _linear_schedule(count, minibatch_count, update_epochs, num_iterations, learning_rate): + frac = 1.0 - (count // (minibatch_count * update_epochs)) / num_iterations + return learning_rate * frac + + +@jax.jit +def _convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray: + return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))( + obs_dict + ) + + +# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit +def _get_action_and_value_noise( + sensor: GenericDenseLayersWithActivation, + feature_extractor: GenericDenseLayersWithActivation, + actor: Actor, + critic: OneDenseLayerMLP, + agent_state: TrainState, + next_obs: jnp.ndarray, + key: jax.random.PRNGKey, +): + hidden = sensor.apply(agent_state.params["sensor_params"], next_obs) + hidden_critic = feature_extractor.apply( + agent_state.params["feature_extractor_params"], next_obs + ) + + # Continuous actions: sample from a Gaussian parameterized by the actor + mean, log_std = actor.apply(agent_state.params["actor_params"], hidden) + key, subkey = jax.random.split(key) + noise = jax.random.normal(subkey, shape=mean.shape) + std = jnp.exp(log_std) + action = mean + noise * std + logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1) + value = critic.apply(agent_state.params["critic_params"], hidden_critic) + return action, logprob, value.squeeze(-1), key + + +# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit +def _step_once( + carry, + _, + env_step_fn, + sensor: GenericDenseLayersWithActivation, + feature_extractor: GenericDenseLayersWithActivation, + actor: Actor, + critic: OneDenseLayerMLP, +): + agent_state, episode_stats, obs, done, key, env_state = carry + action, logprob, value, key = _get_action_and_value_noise( + sensor, feature_extractor, actor, critic, agent_state, obs, key + ) + + episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn( + episode_stats, env_state, action + ) + + storage = Storage( + obs=obs, + actions=action, + logprobs=logprob, + dones=done, + values=value, + rewards=reward, + returns=jnp.zeros_like(reward), + advantages=jnp.zeros_like(reward), + ) + return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage + + +# removed jit: used in _rollout_jit, so will be compiled with _rollout_jit +def _step_env_wrapped(episode_stats, env_state, action, env_step_fn): + next_env_state = env_step_fn(env_state, action) + + # Extract per-environment signals from the state object + reward = next_env_state.reward # (num_envs,) + terminated = next_env_state.terminated # (num_envs,) + truncated = next_env_state.truncated # (num_envs,) + done = terminated | truncated # (num_envs,) + + new_episode_return = episode_stats.episode_returns + reward + new_episode_length = episode_stats.episode_lengths + 1 + + episode_stats = episode_stats.replace( + episode_returns=new_episode_return * (1 - done), + episode_lengths=new_episode_length * (1 - done), + returned_episode_returns=jnp.where( + done, new_episode_return, episode_stats.returned_episode_returns + ), + returned_episode_lengths=jnp.where( + done, new_episode_length, episode_stats.returned_episode_lengths + ), + ) + return ( + episode_stats, + next_env_state, + (_convert_obs_dict_to_array(next_env_state.observations), reward, done), + ) + + +# jit applied in wrapper method self._rollout_jit using partial +def _rollout_jit( + agent_state, + episode_stats, + env_state, + next_obs, + next_done, + key, + max_steps, + step_env_fn, + sensor: GenericDenseLayersWithActivation, + feature_extractor: GenericDenseLayersWithActivation, + actor: Actor, + critic: OneDenseLayerMLP, +): + (agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan( + partial( + _step_once, + sensor=sensor, + feature_extractor=feature_extractor, + actor=actor, + critic=critic, + env_step_fn=step_env_fn, + ), + (agent_state, episode_stats, next_obs, next_done, key, env_state), + (), + max_steps, + ) + return agent_state, episode_stats, next_obs, next_done, storage, key, env_state + + +# removed jit: used in _compute_gae_jit, so will be compiled with _compute_gae_jit +def _compute_gae_once(carry, inp, gamma, gae_lambda): + advantages = carry + nextdone, nextvalues, curvalues, reward = inp + nextnonterminal = 1.0 - nextdone + delta = reward + gamma * nextvalues * nextnonterminal - curvalues + advantages = delta + gamma * gae_lambda * nextnonterminal * advantages + return advantages, advantages + + +# jit applied on partial-wrapped wrapper method self._compute_gae_jit +def _compute_gae_jit( + agent_state, storage, next_obs, next_done, gamma, gae_lambda, num_envs, sensor, critic +): + next_value = critic.apply( + agent_state.params["critic_params"], + sensor.apply(agent_state.params["sensor_params"], next_obs), + ).squeeze(-1) + + advantages = jnp.zeros((num_envs,)) + dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0) + values = jnp.concatenate([storage.values, next_value[None, :]], axis=0) + _, advantages = jax.lax.scan( + partial(_compute_gae_once, gamma=gamma, gae_lambda=gae_lambda), + advantages, + (dones[1:], values[1:], values[:-1], storage.rewards), + reverse=True, + ) + return storage.replace(advantages=advantages, returns=advantages + storage.values) + + +@dataclass +class LossInfo: + # todo: better typing + loss: Any + pg_loss: Any + v_loss: Any + entropy_loss: Any + approx_kl: Any + avg_episodic_return: Any + + +class PPOTrainer: + def __init__(self, args: PPOArgs, env: BrittleStarJaxEnvWrapper, run_dir: str, run_name: str): + self.args = args + self.env = env + self.run_dir = run_dir + self.run_name = run_name + self.writer = SummaryWriter(self.run_dir) + + self.key = jax.random.PRNGKey(args.seed) + + self.sensor, self.feature_extractor, self.actor, self.critic = self._init_agent() + self.sensor.apply = jax.jit(self.sensor.apply) + self.feature_extractor.apply = jax.jit(self.feature_extractor.apply) + self.actor.apply = jax.jit(self.actor.apply) + self.critic.apply = jax.jit(self.critic.apply) + + self._rollout_jit = jax.jit( + partial( + _rollout_jit, + max_steps=self.args.num_steps, + step_env_fn=partial(_step_env_wrapped, env_step_fn=self.env.step), + sensor=self.sensor, + feature_extractor=self.feature_extractor, + actor=self.actor, + critic=self.critic, + ) + ) + self._compute_gae_jit = jax.jit( + partial( + _compute_gae_jit, + num_envs=self.args.num_envs, + gamma=self.args.gamma, + gae_lambda=self.args.gae_lambda, + sensor=self.sensor, + critic=self.critic, + ) + ) + + self._ppo = PPO(self.args, self.sensor, self.actor, self.critic, self.feature_extractor) + + self.agent_state = self._init_agent_state() + + self.episode_stats = self._init_episode_stats() + + self._init_random() + + def _init_random(self, log: bool = True): + if log: + print(f"[RANDOM]: Setting random seed to {self.args.seed}") + + random.seed(self.args.seed) + np.random.seed(self.args.seed) + + def _init_agent(self, log: bool = True): + if log: + print("[AGENT]: Initializing agent...") + + sensor = GenericDenseLayersWithActivation() + feature_extractor = GenericDenseLayersWithActivation() + actor = Actor( + action_dim=self.env.single_action_space.shape[0] + ) # continuous actions for MJX + critic = OneDenseLayerMLP() + # messenger = OneDenseLayerMLP() + return sensor, feature_extractor, actor, critic + + def _init_agent_state(self, log: bool = True) -> TrainState: + if log: + print("[AGENT STATE]: Initializing agent state...") + + self.key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split( + self.key, 5 + ) + + sample_obs = jnp.concatenate( + [ + v.flatten() + for v in self.env.single_observation_space.sample( + rng=jax.random.PRNGKey(0) + ).values() + if v.size > 0 + ] + ) + sensor_params = self.sensor.init(sensor_key, sample_obs) + feature_extractor_params = self.feature_extractor.init(feature_extractor_key, sample_obs) + actor_params = self.actor.init(actor_key, self.sensor.apply(sensor_params, sample_obs)) + critic_params = self.critic.init( + critic_key, self.feature_extractor.apply(feature_extractor_params, sample_obs) + ) + + return TrainState.create( + apply_fn=None, + params=asdict( + AgentParams(sensor_params, actor_params, critic_params, feature_extractor_params) + ), + tx=optax.chain( + optax.clip_by_global_norm(self.args.max_grad_norm), + optax.inject_hyperparams(optax.adam)( + learning_rate=partial( + _linear_schedule, + minibatch_count=self.args.num_minibatches, + update_epochs=self.args.update_epochs, + num_iterations=self.args.num_iterations, + learning_rate=self.args.learning_rate, + ) + if self.args.anneal_lr + else self.args.learning_rate, + eps=1e-5, + ), + ), + ) + + def _init_episode_stats(self, log: bool = True) -> EpisodeStatistics: + if log: + print("[EPISODE STATS]: Initializing episode stats...") + + return EpisodeStatistics( + episode_returns=jnp.zeros(self.args.num_envs, dtype=jnp.float32), + episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32), + returned_episode_returns=jnp.zeros(self.args.num_envs, jnp.float32), + returned_episode_lengths=jnp.zeros(self.args.num_envs, dtype=jnp.int32), + ) + + def _rollout(self, env_state, next_obs, next_done) -> tuple[Storage, ...]: + return self._rollout_jit( + self.agent_state, + self.episode_stats, + env_state, + next_obs, + next_done, + self.key, + ) + + def _compute_gae(self, storage, next_obs, next_done) -> Storage: + return self._compute_gae_jit( + self.agent_state, + storage, + next_obs, + next_done, + ) + + def _log( + self, + global_step, + episode_stats, + start_time, + iteration_time_start, + loss_info, + ): + + self.writer.add_scalar( + "charts/avg_episodic_return", loss_info.avg_episodic_return, global_step + ) + self.writer.add_scalar( + "charts/avg_episodic_length", + np.mean(jax.device_get(episode_stats.returned_episode_lengths)), + global_step, + ) + self.writer.add_scalar( + "charts/learning_rate", + self.agent_state.opt_state[1].hyperparams["learning_rate"].item(), + global_step, + ) + self.writer.add_scalar("losses/value_loss", loss_info.v_loss[-1, -1].item(), global_step) + self.writer.add_scalar("losses/policy_loss", loss_info.pg_loss[-1, -1].item(), global_step) + self.writer.add_scalar("losses/entropy", loss_info.entropy_loss[-1, -1].item(), global_step) + self.writer.add_scalar("losses/approx_kl", loss_info.approx_kl[-1, -1].item(), global_step) + self.writer.add_scalar("losses/loss", loss_info.loss[-1, -1].item(), global_step) + self.writer.add_scalar( + "charts/SPS", int(global_step / (time.time() - start_time)), global_step + ) + self.writer.add_scalar( + "charts/SPS_update", + int(self.args.num_envs * self.args.num_steps / (time.time() - iteration_time_start)), + global_step, + ) + + def _step( + self, env_state, next_obs, next_done, is_tty: bool, iteration: int, log: bool = True + ) -> tuple: + if log and not is_tty and iteration == 1: + print(f">>> [HPC] Starting first rollout (JIT): {time.ctime()}", flush=True) + + ( + self.agent_state, + self.episode_stats, + next_obs, + next_done, + storage, + self.key, + next_env_state, + ) = self._rollout(env_state, next_obs, next_done) + + if log and not is_tty and iteration == 1: + print(f">>> [HPC] First rollout completed: {time.ctime()}", flush=True) + + storage = self._compute_gae(storage, next_obs, next_done) + + if log and not is_tty and iteration == 1: + print(f">>> [HPC] Starting first PPO update (JIT): {time.ctime()}", flush=True) + + self.agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, self.key = ( + self._ppo.update_ppo(self.agent_state, storage, self.key) + ) + + if log and not is_tty and iteration == 1: + print(f">>> [HPC] First PPO update completed: {time.ctime()}", flush=True) + + avg_episodic_return = float( + jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns)).item() + ) + + return ( + next_env_state, + next_obs, + next_done, + LossInfo( + loss=loss, + pg_loss=pg_loss, + v_loss=v_loss, + entropy_loss=entropy_loss, + approx_kl=approx_kl, + avg_episodic_return=avg_episodic_return, + ), + ) + + def _close(self): + self.env.close() + self.writer.close() + + def _save_model(self, model_path: str, log: bool = True): + if log: + print(f"[SAVE]: Saving the model to: {model_path}...") + + with open(model_path, "wb") as f: + f.write( + flax.serialization.to_bytes( + [ + vars(self.args), + [ + self.agent_state.params["sensor_params"], + self.agent_state.params["actor_params"], + self.agent_state.params["critic_params"], + self.agent_state.params["feature_extractor_params"], + ], + ] + ) + ) + + def train(self, log: bool = True): + """ + Train the PPO agent for a specified number of iterations + (passed through PPOArgs in constructor). + Closes the environment at the end of training. + """ + if log: + print(f"running name: {self.run_name}") + + is_tty = sys.stdout.isatty() + if log: + print("[TRAIN]: Resetting environment...") + + if not is_tty: + print(f">>> [HPC] Initial reset started: {time.ctime()}", flush=True) + + env_state = self.env.reset(seed=self.args.seed) + next_obs = _convert_obs_dict_to_array(env_state.observations) + next_done = jnp.zeros(self.args.num_envs, dtype=jnp.bool_) + + if log and not is_tty: + print(f">>> [HPC] Initial reset completed: {time.ctime()}", flush=True) + + global_step = 0 + start_time = time.time() + + if self.args.track: + import wandb + + if log: + print("[TRAIN]: Initializing Weights and Biases...") + + wandb.init( + project=self.args.wandb_project_name, + entity=self.args.wandb_entity, + sync_tensorboard=True, + config=vars(self.args), + name=self.run_name, + save_code=True, + ) + + if log: + print("[TRAIN]: Adding hyperparameters to TensorBoard...") + + self.writer.add_text( + "hyperparameters", + "|param|value|\n|---|---|\n" + + "\n".join(f"|{k}|{v}|" for k, v in vars(self.args).items()), + ) + + iter_bar = tqdm.tqdm( + range(1, self.args.num_iterations + 1), + disable=not is_tty, + ) + for iteration in iter_bar: + iteration_time_start = time.time() + + env_state, next_obs, next_done, loss_info = self._step( + env_state, next_obs, next_done, is_tty=is_tty, iteration=iteration + ) + + global_step += self.args.num_steps * self.args.num_envs + self._log(global_step, self.episode_stats, start_time, iteration_time_start, loss_info) + + if log and not is_tty: + sps = int(global_step / (time.time() - start_time)) + remaining_steps = self.args.total_timesteps - global_step + eta_seconds = int(remaining_steps / sps) if sps > 0 else 0 + eta_str = str(datetime.timedelta(seconds=eta_seconds)) + + print( + f"Iteration {iteration}/{self.args.num_iterations} | " + f"Step {global_step}/{self.args.total_timesteps} | " + f"SPS {sps} | " + f"Return {loss_info.avg_episodic_return:.4f} | " + f"ETA {eta_str}", + flush=True, + ) + + if self.args.save_model: + model_path = f"{self.run_dir}/{self.args.exp_name}.cleanrl_model" + self._save_model(model_path=model_path) + + self._close() diff --git a/src/brittle_star_project/trainers/__init__.py b/src/brittle_star_project/trainers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/train.py b/src/train.py deleted file mode 100644 index 21c2b9b..0000000 --- a/src/train.py +++ /dev/null @@ -1,413 +0,0 @@ -import datetime -import random -import yaml -import subprocess -import sys -import time -from dataclasses import asdict -from functools import partial -from typing import Callable - -import flax -import jax -import jax.numpy as jnp -import matplotlib.pyplot as plt -import numpy as np -import optax -import torch -import tqdm -import tyro -from flax.training.train_state import TrainState -from torch.utils.tensorboard import SummaryWriter - -from brittle_star_project.dataclasses import PPOArgs -from brittle_star_project.dataclasses.EpisodeStatistics import EpisodeStatistics -from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper -from MLPs.mlps import ( - GenericDenseLayersWithActivation, - Actor, - OneDenseLayerMLP, - AgentParams, - Storage, -) -from ppo import PPO - - -def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray: - return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))( - obs_dict - ) - - -def make_env(env_config_path: str | None, num_envs: int) -> Callable: - def thunk(): - if env_config_path is None: - return BrittleStarJaxEnvWrapper.default(num_envs=num_envs) - return BrittleStarJaxEnvWrapper.from_config(env_config_path, num_envs=num_envs) - - return thunk - - -def train(args: PPOArgs): - args.batch_size = args.num_envs * args.num_steps - args.minibatch_size = args.batch_size // args.num_minibatches - args.num_iterations = args.total_timesteps // args.batch_size - - # Try to get git short hash - try: - git_hash = ( - subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip() - ) - except Exception: - git_hash = "none" - - run_name = f"{args.exp_name}__seed_{args.seed}__{git_hash}__{int(time.time())}" - print(f"running name: {run_name}") - - if args.run_dir is None: - args.run_dir = f"runs/{run_name}" - - import os - - os.makedirs(args.run_dir, exist_ok=True) - - if args.track: - import wandb - - wandb.init( - project=args.wandb_project_name, - entity=args.wandb_entity, - sync_tensorboard=True, - config=vars(args), - name=run_name, - save_code=True, - ) - - writer = SummaryWriter(args.run_dir) - writer.add_text( - "hyperparameters", - "|param|value|\n|---|---|\n" + "\n".join(f"|{k}|{v}|" for k, v in vars(args).items()), - ) - - random.seed(args.seed) - np.random.seed(args.seed) - key = jax.random.PRNGKey(args.seed) - key, sensor_key, actor_key, critic_key, feature_extractor_key = jax.random.split(key, 5) - - torch.backends.cudnn.deterministic = args.torch_deterministic - device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") - print(f"Running on device: {device}") - - print("Creating the environment...") - env = make_env(env_config_path=args.env_config_path, num_envs=args.num_envs)() - print(f"Environment: {env}") - - episode_stats = EpisodeStatistics( - episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32), - episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), - returned_episode_returns=jnp.zeros(args.num_envs, jnp.float32), - returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), - ) - - def step_env_wrapped(episode_stats: EpisodeStatistics, env_state, action): - next_env_state = env.step(env_state, action) - - # Extract per-environment signals from the state object - reward = next_env_state.reward # (num_envs,) - terminated = next_env_state.terminated # (num_envs,) - truncated = next_env_state.truncated # (num_envs,) - done = terminated | truncated # (num_envs,) - - new_episode_return = episode_stats.episode_returns + reward - new_episode_length = episode_stats.episode_lengths + 1 - - episode_stats = episode_stats.replace( - episode_returns=new_episode_return * (1 - done), - episode_lengths=new_episode_length * (1 - done), - returned_episode_returns=jnp.where( - done, new_episode_return, episode_stats.returned_episode_returns - ), - returned_episode_lengths=jnp.where( - done, new_episode_length, episode_stats.returned_episode_lengths - ), - ) - return ( - episode_stats, - next_env_state, - (convert_obs_dict_to_array(next_env_state.observations), reward, done), - ) - - def linear_schedule(count): - frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_iterations - return args.learning_rate * frac - - print("Initializing the models...") - sensor = GenericDenseLayersWithActivation() - feature_extractor = GenericDenseLayersWithActivation() - actor = Actor(action_dim=env.single_action_space.shape[0]) # continuous actions for MJX - critic = OneDenseLayerMLP() - # messager = OneDenseLayerMLP() - - sample_obs = jnp.concatenate( - [ - v.flatten() - for v in env.single_observation_space.sample(rng=jax.random.PRNGKey(0)).values() - if v.size > 0 - ] - ) - sensor_params = sensor.init(sensor_key, sample_obs) - feature_extractor_params = feature_extractor.init(feature_extractor_key, sample_obs) - actor_params = actor.init(actor_key, sensor.apply(sensor_params, sample_obs)) - critic_params = critic.init( - critic_key, feature_extractor.apply(feature_extractor_params, sample_obs) - ) - - agent_state = TrainState.create( - apply_fn=None, - params=asdict( - AgentParams(sensor_params, actor_params, critic_params, feature_extractor_params) - ), - tx=optax.chain( - optax.clip_by_global_norm(args.max_grad_norm), - optax.inject_hyperparams(optax.adam)( - learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5 - ), - ), - ) - - sensor.apply = jax.jit(sensor.apply) - feature_extractor.apply = jax.jit(feature_extractor.apply) - actor.apply = jax.jit(actor.apply) - critic.apply = jax.jit(critic.apply) - ppo_instance = PPO(args, sensor, actor, critic, feature_extractor) - - @jax.jit - def get_action_and_value_noise( - agent_state: TrainState, - next_obs: jnp.ndarray, - key: jax.random.PRNGKey, - ): - hidden = sensor.apply(agent_state.params["sensor_params"], next_obs) - hidden_critic = feature_extractor.apply( - agent_state.params["feature_extractor_params"], next_obs - ) - - # Continuous actions: sample from a Gaussian parameterized by the actor - mean, log_std = actor.apply(agent_state.params["actor_params"], hidden) - key, subkey = jax.random.split(key) - noise = jax.random.normal(subkey, shape=mean.shape) - std = jnp.exp(log_std) - action = mean + noise * std - logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1) - value = critic.apply(agent_state.params["critic_params"], hidden_critic) - return action, logprob, value.squeeze(-1), key - - @jax.jit - def compute_gae_once(carry, inp, gamma, gae_lambda): - advantages = carry - nextdone, nextvalues, curvalues, reward = inp - nextnonterminal = 1.0 - nextdone - delta = reward + gamma * nextvalues * nextnonterminal - curvalues - advantages = delta + gamma * gae_lambda * nextnonterminal * advantages - return advantages, advantages - - @jax.jit - def compute_gae(agent_state, next_obs, next_done, storage): - next_value = critic.apply( - agent_state.params["critic_params"], - sensor.apply(agent_state.params["sensor_params"], next_obs), - ).squeeze(-1) - - advantages = jnp.zeros((args.num_envs,)) - dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0) - values = jnp.concatenate([storage.values, next_value[None, :]], axis=0) - _, advantages = jax.lax.scan( - partial(compute_gae_once, gamma=args.gamma, gae_lambda=args.gae_lambda), - advantages, - (dones[1:], values[1:], values[:-1], storage.rewards), - reverse=True, - ) - return storage.replace(advantages=advantages, returns=advantages + storage.values) - - # --- Main training loop --- - global_step = 0 - start_time = time.time() - - # Reset once to get initial state - print("Resetting the environment...") - if not sys.stdout.isatty(): - print(f">>> [HPC] Initial reset started: {time.ctime()}", flush=True) - next_env_state = env.reset(seed=args.seed) - next_obs = convert_obs_dict_to_array(next_env_state.observations) - next_done = jnp.zeros(args.num_envs, dtype=jnp.bool_) - if not sys.stdout.isatty(): - print(f">>> [HPC] Initial reset completed: {time.ctime()}", flush=True) - - def step_once(carry, _, env_step_fn): - agent_state, episode_stats, obs, done, key, env_state = carry - action, logprob, value, key = get_action_and_value_noise(agent_state, obs, key) - - episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn( - episode_stats, env_state, action - ) - - storage = Storage( - obs=obs, - actions=action, - logprobs=logprob, - dones=done, - values=value, - rewards=reward, - returns=jnp.zeros_like(reward), - advantages=jnp.zeros_like(reward), - ) - return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage - - def rollout( - agent_state, episode_stats, next_obs, next_done, key, env_state, step_once_fn, max_steps - ): - (agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan( - step_once_fn, - (agent_state, episode_stats, next_obs, next_done, key, env_state), - (), - max_steps, - ) - return agent_state, episode_stats, next_obs, next_done, storage, key, env_state - - rollout = partial( - rollout, - step_once_fn=partial(step_once, env_step_fn=step_env_wrapped), - max_steps=args.num_steps, - ) - - print("Starting training...") - iters_bar = tqdm.tqdm( - range(1, args.num_iterations + 1), - disable=not sys.stdout.isatty(), - ) - losses = [] - is_tty = sys.stdout.isatty() - for iteration in iters_bar: - iteration_time_start = time.time() - - if not is_tty and iteration == 1: - print(f">>> [HPC] Starting first rollout (JIT): {time.ctime()}", flush=True) - - agent_state, episode_stats, next_obs, next_done, storage, key, next_env_state = rollout( - agent_state, episode_stats, next_obs, next_done, key, next_env_state - ) - - if not is_tty and iteration == 1: - print(f">>> [HPC] First rollout completed: {time.ctime()}", flush=True) - - global_step += args.num_steps * args.num_envs - storage = compute_gae(agent_state, next_obs, next_done, storage) - - if not is_tty and iteration == 1: - print(f">>> [HPC] Starting first PPO update (JIT): {time.ctime()}", flush=True) - - agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = ppo_instance.update_ppo( - agent_state, storage, key - ) - - if not is_tty and iteration == 1: - print(f">>> [HPC] First PPO update completed: {time.ctime()}", flush=True) - - losses.append(jnp.mean(loss)) - - avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns)) - iters_bar.set_postfix_str( - f"global_step={global_step}, avg_episodic_return={avg_episodic_return}" - ) - - writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step) - writer.add_scalar( - "charts/avg_episodic_length", - np.mean(jax.device_get(episode_stats.returned_episode_lengths)), - global_step, - ) - writer.add_scalar( - "charts/learning_rate", - agent_state.opt_state[1].hyperparams["learning_rate"].item(), - global_step, - ) - writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step) - writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step) - writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step) - - # iters_bar.set_postfix_str(f"SPS: {int(global_step / (time.time() - start_time))}") - - writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) - writer.add_scalar( - "charts/SPS_update", - int(args.num_envs * args.num_steps / (time.time() - iteration_time_start)), - global_step, - ) - - if not is_tty: - sps = int(global_step / (time.time() - start_time)) - remaining_steps = args.total_timesteps - global_step - eta_seconds = int(remaining_steps / sps) if sps > 0 else 0 - eta_str = str(datetime.timedelta(seconds=eta_seconds)) - - print( - f"Iteration {iteration}/{args.num_iterations} | " - f"Step {global_step}/{args.total_timesteps} | " - f"SPS {sps} | " - f"Return {avg_episodic_return:.4f} | " - f"ETA {eta_str}", - flush=True, - ) - - if args.save_model: - model_path = f"{args.run_dir}/{args.exp_name}.cleanrl_model" - with open(model_path, "wb") as f: - f.write( - flax.serialization.to_bytes( - [ - vars(args), - [ - agent_state.params["sensor_params"], - agent_state.params["actor_params"], - agent_state.params["critic_params"], - agent_state.params["feature_extractor_params"], - ], - ] - ) - ) - print(f"model saved to {model_path}") - - env.close() - writer.close() - - print("Saving loss plot...") - plt.plot(losses) - plt.title("PPO Loss, mean over minibatches") - plt.savefig(f"{args.run_dir}/{args.exp_name}_losses.png") - plt.close() - - -def main() -> None: - temp_args = tyro.cli(PPOArgs) - - if temp_args.env_config_path is not None: - with open(temp_args.env_config_path, "r") as f: - config = yaml.safe_load(f) - if config: - # parse PPOArgs with defaults from yaml. - for key, value in config.items(): - if hasattr(temp_args, key): - setattr(temp_args, key, value) - - # Re-parse CLI to ensure they OVERRIDE the yaml - args = tyro.cli(PPOArgs, default=temp_args) - else: - args = temp_args - - train(args) - - -if __name__ == "__main__": - main() diff --git a/uv.lock b/uv.lock index 7fbaacb..fd45cfc 100644 --- a/uv.lock +++ b/uv.lock @@ -35,6 +35,9 @@ dependencies = [ ] [package.optional-dependencies] +analysis = [ + { name = "tensorboard" }, +] cuda = [ { name = "jax", extra = ["cuda13"] }, ] @@ -64,12 +67,13 @@ requires-dist = [ { name = "protobuf", specifier = ">=5.0.0" }, { name = "pyopengl", specifier = ">=3.1.10" }, { name = "pyopengl-accelerate", specifier = ">=3.1.10" }, + { name = "tensorboard", marker = "extra == 'analysis'" }, { name = "torch", specifier = ">=2.4.0" }, { name = "tyro", specifier = ">=1.0.10" }, { name = "wandb", specifier = "==0.24.2" }, { name = "warp-lang" }, ] -provides-extras = ["cuda"] +provides-extras = ["cuda", "analysis"] [package.metadata.requires-dev] dev = [